information retrieval using an ontological web-trading model
DESCRIPTION
FedCSIS, ASIR’13, Kraków, POLAND8-11 September, 2013TRANSCRIPT
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3rd International Workshop on Advances in Semantic Information RetrievalKraków, POLAND, 8-11 September, 2013
Information Retrieval Using anOntological Web-Trading Model
José-Andrés Asensio, Nicolás Padilla, Luis Iribarne{jacortes, npadilla, luis.iribarne}@ual.es
Applied Computing Group (TIC-211)University of Almería, SPAIN
FedCSIS, ASIR’13, Kraków, POLAND8-11 September, 2013
Project TIN2010-15588 / Project TIC-6114
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3rd International Workshop on Advances in Semantic Information RetrievalKraków, POLAND, 8-11 September, 2013
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1. Context
2. OWT Model Properties
3. OWT Model Operation
4. Case Study: SOLERES System
5. WTA Implementation
6. Conclusions and Future Work
Contents
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1. Context (1/4)
• Hypothesis:
Systems
– Specific systems.
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1. Context (1/4)
• Hypothesis:
Systems
– Specific systems.
– Huge amount of informationdispersed in multiple sources.
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1. Context (1/4)
• Hypothesis:
Systems
– Specific systems.
– Huge amount of informationdispersed in multiple sources.
– Heterogeneous info. (but structured data).
Source n
Source 5
Source 1
Source 2
Source 3
Source 4
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1. Context (1/4)
• Hypothesis:
Systems
Complexity of the informationsearching/retrieval processes
– Specific systems.
– Huge amount of informationdispersed in multiple sources.
– Heterogeneous info. (but structured data).
Source n
Source 5
Source 1
Source 2
Source 3
Source 4
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1. Context (2/4)
• Solution:
Source n
Source 5
Source 1
Source 2
Source 3
Source 4
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1. Context (2/4)
• Solution:
– Trading Service (Trader).
Source n
Source 5
Source 1
Source 2
Source 3
Source 4
Trader
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1. Context (2/4)
• Solution:
– Trading Service (Trader).
Source n
Source 5
Source 1
Source 2
Source 3
Source 4
Trader
KRS
Knowledge RepresentationSystem (KRS)
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1. Context (2/4)
• Solution:
– Knowledge RepresentationSystem (KRS).
Source n
Source 5
Source 1
Source 2
Source 3
Source 4
Trader
KRS
Model-DrivenEngineering (MDE):> System models <
Ontology-DrivenEngineering (ODE):
> Data ontologies <> Service ontologies <
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1. Context (3/4)
• Retrieval mechanism Query–Searching / Recovering–Response Model:
• Query process of creating and formulating the request.• Searching process of locating the data sources.• Recovering process of selecting the data from the sources.• Response process of creation of the response to the user.
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1. Context (3/4)
• Retrieval mechanism Query–Searching / Recovering–Response Model:
• Query process of creating and formulating the request.• Searching process of locating the data sources.• Recovering process of selecting the data from the sources.• Response process of creation of the response to the user.
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1. Context (3/4)
• Retrieval mechanism Query–Searching / Recovering–Response Model:
• Query process of creating and formulating the request.• Searching process of locating the data sources.• Recovering process of selecting the data from the sources.• Response process of creation of the response to the user.
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1. Context (3/4)
• Retrieval mechanism Query–Searching / Recovering–Response Model:
• Query process of creating and formulating the request.• Searching process of locating the data sources.• Recovering process of selecting the data from the sources.• Response process of creation of the response to the user.
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1. Context (3/4)
• Retrieval mechanism Query–Searching / Recovering–Response Model:
• Query process of creating and formulating the request.• Searching process of locating the data sources.• Recovering process of selecting the data from the sources.• Response process of creation of the response to the user.
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1. Context (4/4)
• From the ODP Trading Function a Trader is a software object that mediates between objects that offer certain capacities or services (Exporters) and other objects that demand their use dynamically (Importers).
• Interfaces:– Lookup.– Register.– Admin.– Link.– Proxy.
Roles
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1. Context (4/4)
• From the ODP Trading Function a Trader is a software object that mediates between objects that offer certain capacities or services (Exporters) and other objects that demand their use dynamically (Importers).
• Interfaces:– Lookup.– Register.– Admin.– Link.– Proxy.
Roles
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1. Context (4/4)
• From the ODP Trading Function a Trader is a software object that mediates between objects that offer certain capacities or services (Exporters) and other objects that demand their use dynamically (Importers).
• Interfaces:– Lookup.– Register.– Admin.– Link.– Proxy.
Roles
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1. Context (4/4)
• From the ODP Trading Function a Trader is a software object that mediates between objects that offer certain capacities or services (Exporters) and other objects that demand their use dynamically (Importers).
• Interfaces:– Lookup.– Register.– Admin.– Link.– Proxy.
Roles
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1. Context (4/4)
• From the ODP Trading Function a Trader is a software object that mediates between objects that offer certain capacities or services (Exporters) and other objects that demand their use dynamically (Importers).
• Interfaces:– Lookup.– Register.– Admin.– Link.– Proxy.
Roles
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1. Context (4/4)
• From the ODP Trading Function a Trader is a software object that mediates between objects that offer certain capacities or services (Exporters) and other objects that demand their use dynamically (Importers).
• Interfaces:– Lookup.– Register.– Admin.– Link.– Proxy.
• Communication Ontologies:– Data Ontologies.– Service Ontologies:
Lookup Ontology. Register Ontology. Admin Ontology. Link Ontology. Proxy Ontology.
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2. OWT Model Properties (1/1)
• Ontological Web-Trading (OWT) Properties:
1. Heterogeneus data model.2. Federation.3. Composition and adaptation of services.4. Weak pairing.5. Usage of heuristics and metrics.6. Extensible and scalable.7. “Storage and forwarding” policy.8. Delegation.9. Push and pull storage model.
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2. OWT Model Properties (1/1)
• Ontological Web-Trading (OWT) Properties:
1. Heterogeneus data model.2. Federation.3. Composition and adaptation of services.4. Weak pairing.5. Usage of heuristics and metrics.6. Extensible and scalable.7. “Storage and forwarding” policy.8. Delegation.9. Push and pull storage model.
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2. OWT Model Properties (1/1)
• Ontological Web-Trading (OWT) Properties:
1. Heterogeneus data model.2. Federation.3. Composition and adaptation of services.4. Weak pairing.5. Usage of heuristics and metrics.6. Extensible and scalable.7. “Storage and forwarding” policy.8. Delegation.9. Push and pull storage model.
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2. OWT Model Properties (1/1)
• Ontological Web-Trading (OWT) Properties:
1. Heterogeneus data model.2. Federation.3. Composition and adaptation of services.4. Weak pairing.5. Usage of heuristics and metrics.6. Extensible and scalable.7. “Storage and forwarding” policy.8. Delegation.9. Push and pull storage model.
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2. OWT Model Properties (1/1)
• Ontological Web-Trading (OWT) Properties:
1. Heterogeneus data model.2. Federation.3. Composition and adaptation of services.4. Weak pairing.5. Usage of heuristics and metrics.6. Extensible and scalable.7. “Storage and forwarding” policy.8. Delegation.9. Push and pull storage model.
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2. OWT Model Properties (1/1)
• Ontological Web-Trading (OWT) Properties:
1. Heterogeneus data model.2. Federation.3. Composition and adaptation of services.4. Weak pairing.5. Usage of heuristics and metrics.6. Extensible and scalable.7. “Storage and forwarding” policy.8. Delegation.9. Push and pull storage model.
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2. OWT Model Properties (1/1)
• Ontological Web-Trading (OWT) Properties:
1. Heterogeneus data model.2. Federation.3. Composition and adaptation of services.4. Weak pairing.5. Usage of heuristics and metrics.6. Extensible and scalable.7. “Storage and forwarding” policy.8. Delegation.9. Push and pull storage model.
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2. OWT Model Properties (1/1)
• Ontological Web-Trading (OWT) Properties:
1. Heterogeneus data model.2. Federation.3. Composition and adaptation of services.4. Weak pairing.5. Usage of heuristics and metrics.6. Extensible and scalable.7. “Storage and forwarding” policy.8. Delegation.9. Push and pull storage model.
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2. OWT Model Properties (1/1)
• Ontological Web-Trading (OWT) Properties:
1. Heterogeneus data model.2. Federation.3. Composition and adaptation of services.4. Weak pairing.5. Usage of heuristics and metrics.6. Extensible and scalable.7. “Storage and forwarding” policy.8. Delegation.9. Push and pull storage model.
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2. OWT Model Properties (1/1)
• Ontological Web-Trading (OWT) Properties:
1. Heterogeneus data model.2. Federation.3. Composition and adaptation of services.4. Weak pairing.5. Usage of heuristics and metrics.6. Extensible and scalable.7. “Storage and forwarding” policy.8. Delegation.9. Push and pull storage model.
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3. OWT Model Operation (1/1)
• Elements:<Interface Object (I), Trading Service (T), System Data (D)>
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3. OWT Model Operation (1/1)
• Elements:<Interface Object (I), Trading Service (T), System Data (D)>
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3. OWT Model Operation (1/1)
• Elements:<Interface Object (I), Trading Service (T), System Data (D)>
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3. OWT Model Operation (1/1)
• Elements:<Interface Object (I), Trading Service (T), System Data (D)>
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4. Case Study: SOLERES System (1/1)
• SOLERES Systemarchitecture:
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4. Case Study: SOLERES System (1/1)
• SOLERES Systemarchitecture:
SOLERES-HCI
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4. Case Study: SOLERES System (1/1)
• SOLERES Systemarchitecture:
SOLERES-KRS
– EID (Environmental Information metaData)
– EIM (EnvironmentalInformation Map)
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5. WTA Implementation (1/3)
• Web-TradingAgent (WTA)view:
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5. WTA Implementation (1/3)
• Web-TradingAgent (WTA)view:
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5. WTA Implementation (1/3)
• Web-TradingAgent (WTA)view:
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5. WTA Implementation (1/3)
• Web-TradingAgent (WTA)view:
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5. WTA Implementation (1/3)
• Web-TradingAgent (WTA)view:
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5. WTA Implementation (2/3)
• Data ontologies EID metadata:
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5. WTA Implementation (2/3)
• Data ontologies EID metadata:
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5. WTA Implementation (2/3)
• Data ontologies EID metadata:
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5. WTA Implementation (2/3)
• Data ontologies EID metadata:
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5. WTA Implementation (2/3)
• Data ontologies EID metadata:
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5. WTA Implementation (2/3)
• Data ontologies EID metadata:
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5. WTA Implementation (2/3)
• Data ontologies EID metadata:
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5. WTA Implementation (3/3)
• Service ontologies Lookup Ontology:
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5. WTA Implementation (3/3)
• Service ontologies Lookup Ontology:
Concepts
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5. WTA Implementation (3/3)
• Service ontologies Lookup Ontology:
Action
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5. WTA Implementation (3/3)
• Service ontologies Lookup Ontology:
Predicates
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6. Conclusions and Future Work (1/1)
• Conclusions:
– Web-based Information Systems (WIS) facilitate information search and retrieval, favoring user cooperation and decision making.
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6. Conclusions and Future Work (1/1)
• Conclusions:
– Web-based Information Systems (WIS) facilitate information search and retrieval, favoring user cooperation and decision making.
– Ontologies provide them a shared vocabulary.– Trading services improve the component interoperability and
information retrieval.
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6. Conclusions and Future Work (1/2)
• Conclusions:
– Web-based Information Systems (WIS) facilitate information search and retrieval, favoring user cooperation and decision making.
– Ontologies provide them a shared vocabulary.– Trading systems improve the component interoperability and
information retrieval.
– We have introduced Ontological Web-Trading (OWT) model as an extension of the traditional ODP trading service.
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6. Conclusions and Future Work (2/2)
• Future work:
– The implementation of SOLERES-HCI by using multi-agent architectures.
– To study how to decompose the user tasks into actions to be performed by the SOLERES-KRS.
– To incorporate evaluation and validation techniques.
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Information Retrieval Using anOntological Web-Trading Model
Thank you for your attention!!Contact: [email protected]
Applied Computing Group (TIC-211)University of Almería, SPAIN
FedCSIS, ASIR’13, Kraków, POLAND8-11 September, 2013
Project TIN2010-15588 / Project TIC-6114
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Contraportada